This repository provides an docker image for the MCCQRNN Model.
Make sure you have installed docker and nvidia-docker.
To get the predictions simply run
$ docker pull ghcr.io/wwu-mmll/mccqrnn_docker
$ docker run -v /path/to/input:/input -v /path/to/otuput:/output ghcr.io/wwu-mmll/mccqrnn_docker
The input directory has to be filled with an input.csv file organized as in the example.csv.
@article{doi:10.1126/sciadv.abg9471,
author = {Tim Hahn and Jan Ernsting and Nils R. Winter and Vincent Holstein and Ramona Leenings and Marie Beisemann and Lukas Fisch and Kelvin Sarink and Daniel Emden and Nils Opel and Ronny Redlich and Jonathan Repple and Dominik Grotegerd and Susanne Meinert and Jochen G. Hirsch and Thoralf Niendorf and Beate Endemann and Fabian Bamberg and Thomas Kröncke and Robin Bülow and Henry Völzke and Oyunbileg von Stackelberg and Ramona Felizitas Sowade and Lale Umutlu and Börge Schmidt and Svenja Caspers and Harald Kugel and Tilo Kircher and Benjamin Risse and Christian Gaser and James H. Cole and Udo Dannlowski and Klaus Berger },
title = {An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling},
journal = {Science Advances},
volume = {8},
number = {1},
pages = {eabg9471},
year = {2022},
doi = {10.1126/sciadv.abg9471},
URL = {https://www.science.org/doi/abs/10.1126/sciadv.abg9471},
eprint = {https://www.science.org/doi/pdf/10.1126/sciadv.abg9471}
,
abstract = { A network-based quantification of brain aging uncovers and fixes a fundamental problem of all previous approaches. The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk marker of cross-disorder brain changes, growing into a cornerstone of biological age research. However, machine learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared because of data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte Carlo dropout composite quantile regression (MCCQR) Neural Network trained on N = 10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared with existing models. In two examples, we demonstrate that it prevents spurious associations and increases power to detect deviant brain aging. We make the pretrained model and code publicly available. }
}